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Showing 373 to 382 of 382 entries
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Next-generation sequencing and empowering personalised cancer medicine.

Drug discovery today

McDermott U.
PMID: 26494142
Drug Discov Today. 2015 Dec;20(12):1470-5. doi: 10.1016/j.drudis.2015.10.008. Epub 2015 Oct 19.

The announcement earlier this year of the US$1000 genome by Illumina has excited a debate as to when and how genomes will at last transition from the research laboratory to the clinic. Although it is still unclear what the...

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McDermott U. Next-generation sequencing and empowering personalised cancer medicine. Drug Discov Today. 2015;20(12):1470-5doi: 10.1016/j.drudis.2015.10.008.
McDermott, U. (2015). Next-generation sequencing and empowering personalised cancer medicine. Drug discovery today, 20(12), 1470-5. https://doi.org/10.1016/j.drudis.2015.10.008
McDermott, Ultan. "Next-generation sequencing and empowering personalised cancer medicine." Drug discovery today vol. 20,12 (2015): 1470-5. doi: https://doi.org/10.1016/j.drudis.2015.10.008
McDermott U. Next-generation sequencing and empowering personalised cancer medicine. Drug Discov Today. 2015 Dec;20(12):1470-5. doi: 10.1016/j.drudis.2015.10.008. Epub 2015 Oct 19. PMID: 26494142.
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TOGGLE: toolbox for generic NGS analyses.

BMC bioinformatics

Monat C, Tranchant-Dubreuil C, Kougbeadjo A, Farcy C, Ortega-Abboud E, Amanzougarene S, Ravel S, Agbessi M, Orjuela-Bouniol J, Summo M, Sabot F.
PMID: 26552596
BMC Bioinformatics. 2015 Nov 09;16:374. doi: 10.1186/s12859-015-0795-6.

BACKGROUND: The explosion of NGS (Next Generation Sequencing) sequence data requires a huge effort in Bioinformatics methods and analyses. The creation of dedicated, robust and reliable pipelines able to handle dozens of samples from raw FASTQ data to relevant...

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Monat C, Tranchant-Dubreuil C, Kougbeadjo A, et al. TOGGLE: toolbox for generic NGS analyses. BMC Bioinformatics. 2015;16:374doi: 10.1186/s12859-015-0795-6.
Monat, C., Tranchant-Dubreuil, C., Kougbeadjo, A., Farcy, C., Ortega-Abboud, E., Amanzougarene, S., Ravel, S., Agbessi, M., Orjuela-Bouniol, J., Summo, M., & Sabot, F. (2015). TOGGLE: toolbox for generic NGS analyses. BMC bioinformatics, 16374. https://doi.org/10.1186/s12859-015-0795-6
Monat, Cécile, et al. "TOGGLE: toolbox for generic NGS analyses." BMC bioinformatics vol. 16 (2015): 374. doi: https://doi.org/10.1186/s12859-015-0795-6
Monat C, Tranchant-Dubreuil C, Kougbeadjo A, Farcy C, Ortega-Abboud E, Amanzougarene S, Ravel S, Agbessi M, Orjuela-Bouniol J, Summo M, Sabot F. TOGGLE: toolbox for generic NGS analyses. BMC Bioinformatics. 2015 Nov 09;16:374. doi: 10.1186/s12859-015-0795-6. PMID: 26552596; PMCID: PMC4640241.
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Integrate Omics Data and Molecular Dynamics Simulations toward Better Understanding of Human 14-3-3 Interactomes and Better Drugs for Cancer Therapy.

Journal of genetics and genomics = Yi chuan xue bao

Babula JJ, Liu JY.
PMID: 26554908
J Genet Genomics. 2015 Oct 20;42(10):531-547. doi: 10.1016/j.jgg.2015.09.002. Epub 2015 Sep 14.

The 14-3-3 protein family is among the most extensively studied, yet still largely mysterious protein families in mammals to date. As they are well recognized for their roles in apoptosis, cell cycle regulation, and proliferation in healthy cells, aberrant...

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Babula JJ, Liu JY. Integrate Omics Data and Molecular Dynamics Simulations toward Better Understanding of Human 14-3-3 Interactomes and Better Drugs for Cancer Therapy. J Genet Genomics. 2015;42(10):531-547doi: 10.1016/j.jgg.2015.09.002.
Babula, J. J., & Liu, J. Y. (2015). Integrate Omics Data and Molecular Dynamics Simulations toward Better Understanding of Human 14-3-3 Interactomes and Better Drugs for Cancer Therapy. Journal of genetics and genomics = Yi chuan xue bao, 42(10), 531-547. https://doi.org/10.1016/j.jgg.2015.09.002
Babula, JoAnne J, and Liu, Jing-Yuan. "Integrate Omics Data and Molecular Dynamics Simulations toward Better Understanding of Human 14-3-3 Interactomes and Better Drugs for Cancer Therapy." Journal of genetics and genomics = Yi chuan xue bao vol. 42,10 (2015): 531-547. doi: https://doi.org/10.1016/j.jgg.2015.09.002
Babula JJ, Liu JY. Integrate Omics Data and Molecular Dynamics Simulations toward Better Understanding of Human 14-3-3 Interactomes and Better Drugs for Cancer Therapy. J Genet Genomics. 2015 Oct 20;42(10):531-547. doi: 10.1016/j.jgg.2015.09.002. Epub 2015 Sep 14. PMID: 26554908.
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Statistical Considerations on NGS Data for Inferring Copy Number Variations.

Methods in molecular biology (Clifton, N.J.)

Chen J.
PMID: 33606251
Methods Mol Biol. 2021;2243:27-58. doi: 10.1007/978-1-0716-1103-6_2.

The next-generation sequencing (NGS) technology has revolutionized research in genetics and genomics, resulting in massive NGS data and opening more fronts to answer unresolved issues in genetics. NGS data are usually stored at three levels: image files, sequence tags,...

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Chen J. Statistical Considerations on NGS Data for Inferring Copy Number Variations. Methods Mol Biol. 2021;2243:27-58doi: 10.1007/978-1-0716-1103-6_2.
Chen, J. (2021). Statistical Considerations on NGS Data for Inferring Copy Number Variations. Methods in molecular biology (Clifton, N.J.), 224327-58. https://doi.org/10.1007/978-1-0716-1103-6_2
Chen, Jie. "Statistical Considerations on NGS Data for Inferring Copy Number Variations." Methods in molecular biology (Clifton, N.J.) vol. 2243 (2021): 27-58. doi: https://doi.org/10.1007/978-1-0716-1103-6_2
Chen J. Statistical Considerations on NGS Data for Inferring Copy Number Variations. Methods Mol Biol. 2021;2243:27-58. doi: 10.1007/978-1-0716-1103-6_2. PMID: 33606251.
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Nebula: ultra-efficient mapping-free structural variant genotyper.

Nucleic acids research

Khorsand P, Hormozdiari F.
PMID: 33503255
Nucleic Acids Res. 2021 May 07;49(8):e47. doi: 10.1093/nar/gkab025.

Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task...

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Khorsand P, Hormozdiari F. Nebula: ultra-efficient mapping-free structural variant genotyper. Nucleic Acids Res. 2021;49(8):e47doi: 10.1093/nar/gkab025.
Khorsand, P., & Hormozdiari, F. (2021). Nebula: ultra-efficient mapping-free structural variant genotyper. Nucleic acids research, 49(8), e47. https://doi.org/10.1093/nar/gkab025
Khorsand, Parsoa, and Hormozdiari, Fereydoun. "Nebula: ultra-efficient mapping-free structural variant genotyper." Nucleic acids research vol. 49,8 (2021): e47. doi: https://doi.org/10.1093/nar/gkab025
Khorsand P, Hormozdiari F. Nebula: ultra-efficient mapping-free structural variant genotyper. Nucleic Acids Res. 2021 May 07;49(8):e47. doi: 10.1093/nar/gkab025. PMID: 33503255; PMCID: PMC8096284.
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MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes.

BMC bioinformatics

Ruiz-Perez CA, Conrad RE, Konstantinidis KT.
PMID: 33407081
BMC Bioinformatics. 2021 Jan 06;22(1):11. doi: 10.1186/s12859-020-03940-5.

BACKGROUND: High-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for...

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Ruiz-Perez CA, Conrad RE, Konstantinidis KT. MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes. BMC Bioinformatics. 2021;22(1):11doi: 10.1186/s12859-020-03940-5.
Ruiz-Perez, C. A., Conrad, R. E., & Konstantinidis, K. T. (2021). MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes. BMC bioinformatics, 22(1), 11. https://doi.org/10.1186/s12859-020-03940-5
Ruiz-Perez, Carlos A, et al. "MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes." BMC bioinformatics vol. 22,1 (2021): 11. doi: https://doi.org/10.1186/s12859-020-03940-5
Ruiz-Perez CA, Conrad RE, Konstantinidis KT. MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes. BMC Bioinformatics. 2021 Jan 06;22(1):11. doi: 10.1186/s12859-020-03940-5. PMID: 33407081; PMCID: PMC7789693.
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A community resource for paired genomic and metabolomic data mining.

Nature chemical biology

Schorn MA, Verhoeven S, Ridder L, Huber F, Acharya DD, Aksenov AA, Aleti G, Moghaddam JA, Aron AT, Aziz S, Bauermeister A, Bauman KD, Baunach M, Beemelmanns C, Beman JM, Berlanga-Clavero MV, Blacutt AA, Bode HB, Boullie A, Brejnrod A, Bugni TS, Calteau A, Cao L, Carrión VJ, Castelo-Branco R, Chanana S, Chase AB, Chevrette MG, Costa-Lotufo LV, Crawford JM, Currie CR, Cuypers B, Dang T, de Rond T, Demko AM, Dittmann E, Du C, Drozd C, Dujardin JC, Dutton RJ, Edlund A, Fewer DP, Garg N, Gauglitz JM, Gentry EC, Gerwick L, Glukhov E, Gross H, Gugger M, Guillén Matus DG, Helfrich EJN, Hempel BF, Hur JS, Iorio M, Jensen PR, Kang KB, Kaysser L, Kelleher NL, Kim CS, Kim KH, Koester I, König GM, Leao T, Lee SR, Lee YY, Li X, Little JC, Maloney KN, Männle D, Martin H C, McAvoy AC, Metcalf WW, Mohimani H, Molina-Santiago C, Moore BS, Mullowney MW, Muskat M, Nothias LF, O'Neill EC, Parkinson EI, Petras D, Piel J, Pierce EC, Pires K, Reher R, Romero D, Roper MC, Rust M, Saad H, Saenz C, Sanchez LM, Sørensen SJ, Sosio M, Süssmuth RD, Sweeney D, Tahlan K, Thomson RJ, Tobias NJ, Trindade-Silva AE, van Wezel GP, Wang M, Weldon KC, Zhang F, Ziemert N, Duncan KR, Crüsemann M, Rogers S, Dorrestein PC, Medema MH, van der Hooft JJJ.
PMID: 33589842
Nat Chem Biol. 2021 Apr;17(4):363-368. doi: 10.1038/s41589-020-00724-z.

No abstract available.

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Schorn MA, Verhoeven S, Ridder L, et al. A community resource for paired genomic and metabolomic data mining. Nat Chem Biol. 2021;17(4):363-368doi: 10.1038/s41589-020-00724-z.
Schorn, M. A., Verhoeven, S., Ridder, L., Huber, F., Acharya, D. D., Aksenov, A. A., Aleti, G., Moghaddam, J. A., Aron, A. T., Aziz, S., Bauermeister, A., Bauman, K. D., Baunach, M., Beemelmanns, C., Beman, J. M., Berlanga-Clavero, M. V., Blacutt, A. A., Bode, H. B., Boullie, A., Brejnrod, A., Bugni, T. S., Calteau, A., Cao, L., Carrión, V. J., Castelo-Branco, R., Chanana, S., Chase, A. B., Chevrette, M. G., Costa-Lotufo, L. V., Crawford, J. M., Currie, C. R., Cuypers, B., Dang, T., de Rond, T., Demko, A. M., Dittmann, E., Du, C., Drozd, C., Dujardin, J. C., Dutton, R. J., Edlund, A., Fewer, D. P., Garg, N., Gauglitz, J. M., Gentry, E. C., Gerwick, L., Glukhov, E., Gross, H., Gugger, M., Guillén Matus, D. G., Helfrich, E. J. N., Hempel, B. F., Hur, J. S., Iorio, M., Jensen, P. R., Kang, K. B., Kaysser, L., Kelleher, N. L., Kim, C. S., Kim, K. H., Koester, I., König, G. M., Leao, T., Lee, S. R., Lee, Y. Y., Li, X., Little, J. C., Maloney, K. N., Männle, D., Martin H, C., McAvoy, A. C., Metcalf, W. W., Mohimani, H., Molina-Santiago, C., Moore, B. S., Mullowney, M. W., Muskat, M., Nothias, L. F., O'Neill, E. C., Parkinson, E. I., Petras, D., Piel, J., Pierce, E. C., Pires, K., Reher, R., Romero, D., Roper, M. C., Rust, M., Saad, H., Saenz, C., Sanchez, L. M., Sørensen, S. J., Sosio, M., Süssmuth, R. D., Sweeney, D., Tahlan, K., Thomson, R. J., Tobias, N. J., Trindade-Silva, A. E., van Wezel, G. P., Wang, M., Weldon, K. C., Zhang, F., Ziemert, N., Duncan, K. R., Crüsemann, M., Rogers, S., Dorrestein, P. C., Medema, M. H., & van der Hooft, J. J. J. (2021). A community resource for paired genomic and metabolomic data mining. Nature chemical biology, 17(4), 363-368. https://doi.org/10.1038/s41589-020-00724-z
Schorn, Michelle A, et al. "A community resource for paired genomic and metabolomic data mining." Nature chemical biology vol. 17,4 (2021): 363-368. doi: https://doi.org/10.1038/s41589-020-00724-z
Schorn MA, Verhoeven S, Ridder L, Huber F, Acharya DD, Aksenov AA, Aleti G, Moghaddam JA, Aron AT, Aziz S, Bauermeister A, Bauman KD, Baunach M, Beemelmanns C, Beman JM, Berlanga-Clavero MV, Blacutt AA, Bode HB, Boullie A, Brejnrod A, Bugni TS, Calteau A, Cao L, Carrión VJ, Castelo-Branco R, Chanana S, Chase AB, Chevrette MG, Costa-Lotufo LV, Crawford JM, Currie CR, Cuypers B, Dang T, de Rond T, Demko AM, Dittmann E, Du C, Drozd C, Dujardin JC, Dutton RJ, Edlund A, Fewer DP, Garg N, Gauglitz JM, Gentry EC, Gerwick L, Glukhov E, Gross H, Gugger M, Guillén Matus DG, Helfrich EJN, Hempel BF, Hur JS, Iorio M, Jensen PR, Kang KB, Kaysser L, Kelleher NL, Kim CS, Kim KH, Koester I, König GM, Leao T, Lee SR, Lee YY, Li X, Little JC, Maloney KN, Männle D, Martin H C, McAvoy AC, Metcalf WW, Mohimani H, Molina-Santiago C, Moore BS, Mullowney MW, Muskat M, Nothias LF, O'Neill EC, Parkinson EI, Petras D, Piel J, Pierce EC, Pires K, Reher R, Romero D, Roper MC, Rust M, Saad H, Saenz C, Sanchez LM, Sørensen SJ, Sosio M, Süssmuth RD, Sweeney D, Tahlan K, Thomson RJ, Tobias NJ, Trindade-Silva AE, van Wezel GP, Wang M, Weldon KC, Zhang F, Ziemert N, Duncan KR, Crüsemann M, Rogers S, Dorrestein PC, Medema MH, van der Hooft JJJ. A community resource for paired genomic and metabolomic data mining. Nat Chem Biol. 2021 Apr;17(4):363-368. doi: 10.1038/s41589-020-00724-z. PMID: 33589842; PMCID: PMC7987574.
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Scalable computing for evolutionary genomics.

Methods in molecular biology (Clifton, N.J.)

Prins P, Belhachemi D, Möller S, Smant G.
PMID: 22399474
Methods Mol Biol. 2012;856:529-45. doi: 10.1007/978-1-61779-585-5_22.

Genomic data analysis in evolutionary biology is becoming so computationally intensive that analysis of multiple hypotheses and scenarios takes too long on a single desktop computer. In this chapter, we discuss techniques for scaling computations through parallelization of calculations,...

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Prins P, Belhachemi D, Möller S, et al. Scalable computing for evolutionary genomics. Methods Mol Biol. 2012;856:529-45doi: 10.1007/978-1-61779-585-5_22.
Prins, P., Belhachemi, D., Möller, S., & Smant, G. (2012). Scalable computing for evolutionary genomics. Methods in molecular biology (Clifton, N.J.), 856529-45. https://doi.org/10.1007/978-1-61779-585-5_22
Prins, Pjotr, et al. "Scalable computing for evolutionary genomics." Methods in molecular biology (Clifton, N.J.) vol. 856 (2012): 529-45. doi: https://doi.org/10.1007/978-1-61779-585-5_22
Prins P, Belhachemi D, Möller S, Smant G. Scalable computing for evolutionary genomics. Methods Mol Biol. 2012;856:529-45. doi: 10.1007/978-1-61779-585-5_22. PMID: 22399474.
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|SE|S|AM|E| Barcode: NGS-oriented software for amplicon characterization--application to species and environmental barcoding.

Molecular ecology resources

Piry S, Guivier E, Realini A, Martin JF.
PMID: 22823139
Mol Ecol Resour. 2012 Nov;12(6):1151-7. doi: 10.1111/j.1755-0998.2012.03171.x. Epub 2012 Jul 23.

Progress in NGS technologies has opened up new opportunities for characterizing biodiversity, both for individual specimen identification and for environmental barcoding. Although the amount of data available to biologist is increasing, user-friendly tools to facilitate data analysis have yet...

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Piry S, Guivier E, Realini A, et al. |SE|S|AM|E| Barcode: NGS-oriented software for amplicon characterization--application to species and environmental barcoding. Mol Ecol Resour. 2012;12(6):1151-7doi: 10.1111/j.1755-0998.2012.03171.x.
Piry, S., Guivier, E., Realini, A., & Martin, J. F. (2012). |SE|S|AM|E| Barcode: NGS-oriented software for amplicon characterization--application to species and environmental barcoding. Molecular ecology resources, 12(6), 1151-7. https://doi.org/10.1111/j.1755-0998.2012.03171.x
Piry, S, et al. "|SE|S|AM|E| Barcode: NGS-oriented software for amplicon characterization--application to species and environmental barcoding." Molecular ecology resources vol. 12,6 (2012): 1151-7. doi: https://doi.org/10.1111/j.1755-0998.2012.03171.x
Piry S, Guivier E, Realini A, Martin JF. |SE|S|AM|E| Barcode: NGS-oriented software for amplicon characterization--application to species and environmental barcoding. Mol Ecol Resour. 2012 Nov;12(6):1151-7. doi: 10.1111/j.1755-0998.2012.03171.x. Epub 2012 Jul 23. PMID: 22823139.
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ANGES: reconstructing ANcestral GEnomeS maps.

Bioinformatics (Oxford, England)

Jones BR, Rajaraman A, Tannier E, Chauve C.
PMID: 22820205
Bioinformatics. 2012 Sep 15;28(18):2388-90. doi: 10.1093/bioinformatics/bts457. Epub 2012 Jul 20.

SUMMARY: ANGES is a suite of Python programs that allows reconstructing ancestral genome maps from the comparison of the organization of extant-related genomes. ANGES can reconstruct ancestral genome maps for multichromosomal linear genomes and unichromosomal circular genomes. It implements...

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Jones BR, Rajaraman A, Tannier E, et al. ANGES: reconstructing ANcestral GEnomeS maps. Bioinformatics. 2012;28(18):2388-90doi: 10.1093/bioinformatics/bts457.
Jones, B. R., Rajaraman, A., Tannier, E., & Chauve, C. (2012). ANGES: reconstructing ANcestral GEnomeS maps. Bioinformatics (Oxford, England), 28(18), 2388-90. https://doi.org/10.1093/bioinformatics/bts457
Jones, Bradley R, et al. "ANGES: reconstructing ANcestral GEnomeS maps." Bioinformatics (Oxford, England) vol. 28,18 (2012): 2388-90. doi: https://doi.org/10.1093/bioinformatics/bts457
Jones BR, Rajaraman A, Tannier E, Chauve C. ANGES: reconstructing ANcestral GEnomeS maps. Bioinformatics. 2012 Sep 15;28(18):2388-90. doi: 10.1093/bioinformatics/bts457. Epub 2012 Jul 20. PMID: 22820205.
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